Data collection techniques and analysis typically rely on large amounts of data entry and data manipulation. A large portion of data entry and data manipulation is performed manually, and is very time consuming. As a non-limiting example, data related to a repeated process (such as a duration thereof or an occurrence of an event) is typically entered manually during the repeated process or shortly after the repeated process occurs. Manual data entry and analysis is prone to error, may be distracting in particular instances, and is not an efficient use of skilled labor.
Systems used to perform data collection and analysis tend to be complex and cost prohibitive. Such systems typically employ proprietary hardware and are limited in configuration and in how data is able to be collected. Additionally, improvements that may be afforded to a process or a system by an analysis of collected data typically require complex and costly adjustments.
It would be advantageous to develop a system and a method for data collection and analysis that uses a multi-level network that automates a substantial portion of data collection and analysis, is adaptable for a wide variety of platforms and devices, and allows for improvements afforded by data analysis to be easily implemented.